A Constructive Graphical Model Approach for Knowledge‐Based Systems: A Vehicle Monitoring Case Study

Abstract

Graphical models have been widely applied to uncertain reasoning in knowledge‐based systems. For many of the problems tackled, a single graphical model is constructed before individual cases are presented and the model is used to reason about each new case. In this work, we consider a class of problems whose solution requires inference over a very large number of models that are impractical to construct a priori. We conduct a case study in the domain of vehicle monitoring and then generalize the approach taken. We show that the previously held negative belief on the applicability of graphical models to such problems is unjustified. We propose a set of techniques based on domain decomposition, model separation, model approximation, model compilation, and re‐analysis to meet the computational challenges imposed by the combinatorial explosion. Experimental results on vehicle monitoring demonstrated good performance at near‐real‐time.